Resilient Smart Grid Architecture Using Fuzzy Optimization and Edge Computing Technologies

Abstract

This research explores the use of IoT, artificial intelligence (AI) and edge computing for real-time optimization, reliability and efficiency improvement of smart grid systems in smart cities. The aim is to create a scalable and smart system to manage varying energy loads, reduce latency, and enhance reliability. The approach is based on an edge-cloud computing architecture with AI-based algorithms, such as deep learning, fuzzy logic, and adaptive control, for real-time monitoring, fault detection, and load management. Computation is done at the edge to avoid transmission delays and enable quicker decision-making, with cloud-based services for long-term processing and system coordination. The study shows substantial gains in energy efficiency, latency, fault tolerance, and grid stability compared to traditional centralized systems. Moreover, the proposed architecture exhibits resilience to cyber-attacks, and facilitates renewable energy integration. The results demonstrate the role of edge intelligence in facilitating decentralized decision making and responsiveness in smart grids. Overall, IoT and edge AI offers a viable approach for the next generation of smart energy systems, with enhanced efficiency, scalability and security. Future research could explore enhanced security, interoperability and massive deployment in diverse settings.